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Galectin-3 and the development of heart failure after acute coronary syndrome: pilot experience from PROVE IT-TIMI 22.

Galectin-3 is a soluble [beta]-galactoside-binding lectin that appears to be a direct mediator of profibrotic pathways and is a potential marker of adverse cardiac remodeling. Galectin-3 is expressed by activated macrophages and induces cardiac fibroblasts to proliferate and deposit type I collagen in the myocardium (1). The expression of galectin-3 is substantially upregulated in animal models of heart failure (HF) [4] and occurs before the development of overt clinical HF (1-3). The protein localizes to areas of fibrosis, suggesting an active role in modulation of the extracellular matrix (1). Moreover, intrapericardial infusion of galectin-3 in healthy and hypertensive rats leads to myocardial macrophage and mast cell infiltration, increased collagen I deposition, and impaired diastolic and systolic ventricular function (1,4).

Given its apparent role in adverse cardiac remodeling, galectin-3 has been examined in patients with HF. In these patients, galectin-3 provides incremental prognostic value over traditional pressure-dependent biomarkers, such as B-type natriuretic peptide (BNP), and was recently cleared by the US Food and Drug Administration for the prognostic assessment of patients with chronic HF (5-11). Patients who develop acute coronary syndrome (ACS) have a significantly increased risk of developing subsequent HF, with substantial associated morbidity and mortality (12, 13). As such, there is strong clinical interest in identifying predictors of new HF in this population. Established clinical risk factors for HF after ACS, including age, decreased left ventricular ejection fraction (LVEF) from baseline, hypertension, diabetes, and previous myocardial infarction, do not account for all of the attributable risk (12-14). In light of its pathobiology and application in patients with HF, we hypothesized that galectin-3 might be useful for assessing the risk of HF in patients with ischemic heart disease. To test this hypothesis, we performed a nested case-control pilot study to determine whether galectin-3 is associated with the risk of developing new or worsening heart failure after ACS.



We performed this nested case-control study with prospectively collected samples and outcomes from the main biomarker substudy within the Pravastatin or Atorvastatin Evaluation and Infection Therapy--Thrombolysis in Myocardial Infarction 22 (PROVE IT-TIMI 22) trial. The design and results of the main trial have been described (15). Patients with unstable angina or myocardial infarction (MI) were randomized to standard or intensive statin therapy with pravastatin 40 mg daily vs atorvastatin 80 mg daily and followed for a mean of 2 years. We identified 100 cases with a hospitalization for new or worsening HF along with 100 control subjects who were matched 1:1 for age, sex, presenting ACS type (unstable angina, non-ST-elevation MI, or ST-elevation MI), and randomized treatment. Hospitalization for new or worsening HF was categorized as hospitalization with a final diagnosis of heart failure supported by evidence of pulmonary congestion on a chest radiograph and fulfillment of 2 of the following 6 criteria: rales in the midlung that did not clear with coughing; LVEF <40%; mean pulmonary capillary wedge pressure [greater than or equal to]18 mmHg and cardiac index [less than or equal to]2.2 L/min x [m.sup.2]; use of diuretics to treat pulmonary congestion in patients not previously taking diuretics or an increase in dose in patients taking diuretics chronically; need for intubation for hypoxia; and oxygen saturation <90% or oxygen pressure (p[O.sub.2]) < 60 mmHg. LVEF was recorded in the case report form when assessed as part of routine care.


Whole blood was obtained from subjects via standard venipuncture, and serum was isolated within 60 min of sampling, shipped overnight refrigerated, and stored at -70[degrees]C or colder until the time of testing. Galectin-3 was measured at enrollment (within 7 days post-ACS) using an ELISA (BG Medicine). This assay has a lower limit of detection of 1.13 [micro]g/L (ng/mL) and demonstrates no cross-reactivity with other galectins or collagens (16). Total imprecision of the assay at concentrations of 17.6 [micro]g/L and 26.3 [micro]g/L is 5.1% and 4.2%, respectively. Prior studies have shown that galectin-3 concentrations are stable for at least 6 months when samples are stored at either -20 [degrees]C or -70 [degrees]C and remain stable through at least 6 freeze-thaw cycles over that time (16). B-type natriuretic peptide (BNP) was measured using the Advia Centaur (Siemens).


Continuous variables were summarized as mean and SD if normally distributed and median with interquartile range if non-normally distributed. We compared baseline characteristics using the [chi square] test for categorical variables and the Wilcoxon rank-sum test for continuous variables. Owing to anticipated skewness of biomarker values, we used paired nonparametric testing to compare galectin-3 concentrations between cases and controls. Patients were both categorized based on quartiles of galectin-3 and dichotomized at the median. We used conditional logistic regression to assess the association between galectin-3 and new or worsening HF. To adjust for traditional risk factors for HF, including hypertension, diabetes, history of HF, prior MI, baseline BNP, and LVEF, we used multivariable conditional logistic regression. We assessed the correlation between galectin-3 and BNP and LVEF using Spearman rank correlation coefficient. Significance was set at a 2-sided P value <0.05. All statistical analyses were performed using STATA version 10.1.



Baseline characteristics for cases and controls are shown in Table 1. Cases were more likely to have preexisting hypertension, a history of HF, and prior MI. Cases were also more likely to have an increased baseline BNP concentrations and systolic dysfunction, defined as LVEF <40%.


Patients who developed HF had a higher baseline galectin-3 concentration [median 16.7 [micro]g/L (25th, 75th percentiles 14.0, 20.6)] vs controls [14.6 [micro]g/L (12.0, 17.6)], P = 0.004. Patients with baseline galectin-3 above the median were twice as likely to develop HF [odds ratio (OR) 2.1 (95% CI 1.2-3.6), P = 0.010]. Galectin-3 showed a graded relationship with the risk of heart failure, and patients in the highest galectin-3 quartile had a 3.6 times higher odds of developing HF than those in the baseline quartile ([P.sub.trend] = 0.003) (Fig. 1).

After adjustment for clinical risk factors for HF, including hypertension, diabetes, prior HF, and prior MI, the significant graded relationship with HF persisted across the quartiles of galectin-3 concentration, with adjusted OR 1.4 per quartile (95% CI 1.1-1.9, P = 0.020), and when modeled as a continuous variable demonstrated a 47% increase in the adjusted relative odds of HF for each SD increase in galectin-3 (P = 0.049). The adjusted OR of HF in patients with galectin-3 concentration above the median was 1.9 (95% CI 1.0-3.5, P = 0.045). Moreover, in an analysis restricted to patients without a history of HF, the significant relationship between galectin-3 quartile and odds of HF remained, with an adjusted OR of 1.4 (95% CI 1.0-2.0, P = 0.038).



Baseline BNP >80 pg/mL was also strongly associated with heart failure [OR 2.8 (95% CI 1.3-6.0), P = 0.009]. Galectin-3 did not significantly correlate with BNP (Spearman [rho] = 0.13, P = 0.08) (Fig. 2). When both BNP and galectin-3 were included in the multivariable model, however, the graded relationship between galectin-3 and HF was attenuated [adjusted OR 1.3 (95% CI 0.96-1.9), P = 0.08]. When patients were stratified by both galectin-3 at the median and BNP at the cut point of 80 pg/mL, patients with increased galectin-3 and BNP had the highest odds of heart failure: OR 4.7 (95% CI 1.7-13.0; P = 0.002) for high BNP/high galectin-3 compared to the referent of low BNP/low galectin-3 (Fig. 3).

There was a weak but significant negative correlation between galectin-3 and LVEF (Spearman [rho] = -0.31, P = 0.0002). When LVEF was added to the multivariable model, the relationship between galectin-3 and HF was attenuated [adjusted OR 1.4 (95% CI 0.9-2.3), P = 0.15].


The results of this pilot study suggest that the concentration of galectin-3 after ACS is associated with the risk of developing subsequent HF. The observed significant graded relationship supports the potential clinical relevance of galectin-3-mediated pathways in patients with ischemic heart disease who develop HF. Together with experimental data, our pilot findings reinforce clinical interest in the active role that galectin-3 appears to play in promoting cardiac fibrosis and adverse remodeling. As a prognostic biomarker in patients with ACS, galectin-3 was associated with HF independently of clinical risk factors but was not as strongly associated as BNP in this small study population.


Galectin-3 appears to link pathways of inflammation and fibrosis and may contribute to the development of HF by mediating progressive alteration of the myocardial extracellular matrix (17). Myocardial injury generates inflammatory signals that recruit activated macrophages to the myocardium. Mediators such as osteopontin stimulate these macrophages to secrete galectin-3 (3), which appears to act through upregulation of the transforming growth factor (TGF)-[beta]/ Smad3 signaling pathway, causing cardiac fibroblasts to proliferate and produce type I collagen, ultimately leading to an accumulation of myocardial collagen and impaired diastolic and systolic function (1, 4).Ina mouse model of desmin-deficient HF, knocking out the gene for osteopontin resulted in dramatically decreased galectin-3 expression and subsequently less cardiac fibrosis and improved echocardiographic parameters of ventricular function (3). In patients with chronic HF, galectin-3 concentrations have been correlated with markers of extracellular matrix turnover, such as type III amino-terminal propeptide of procollagen (PIIINP) and matrix metalloproteinase 2 (MMP2), implying a relationship between macrophage activation and collagen turnover (18). Intriguingly, N-acetyl-seryl-aspartyl-lysyl-proline (Ac-SDKP), a monocyte-derived antiinflammatory and antifibrotic peptide that is degraded by ACE, inhibits galectin-3 expression in the left ventricle and reduces galectin-3-induced macrophage activation and migration (19).In a rat model, intrapericardial infusion of Ac-SDKP mitigated many of the adverse effects of galectin-3, leading to reduced myocardial macrophage and mast cell density, decreased left ventricular collagen burden, and improved diastolic and systolic function (4). These data suggest that the adverse consequences associated with galectin-3 maybe modifiable.

Galectin-3 may be particularly important in patients with ischemic heart disease in whom there is sufficient inflammatorysubstrate for macrophage recruitment and subsequent galectin-3 secretion. In patients with advanced decompensated heart failure, increased galectin-3 concentrations were significantly correlated with an ischemic etiology of HF (10). Overall, our findings add to the evidence supporting the relevance of galectin-3 in patients with ischemic heart disease and the investigation of galectin-3-related pathways as a potential target for therapeutic intervention. Given our observation that galectin-3 is related to the risk of HF after ACS, it is reasonable to hypothesize that therapies which impact adverse remodeling, such as ACE inhibitors or aldosterone receptor antagonists, might be particularly-beneficial in patients with increased galectin-3 after ACS. This hypothesis requires investigation before such an application would be entertained for clinical use.


Galectin-3 has been validated as a biomarker with independent prognostic value in patients with both acute and chronic HF. Data from the ProBNP Investigation of Dyspnea in the Emergency Department (PRIDE) study, which examined 599 patients presenting to the emergency room with acute dyspnea, demonstrated that among the 209 patients ultimately diagnosed with acute HF, baseline galectin-3 was a superior predictor of short-term (60-day) mortality than NT-proBNP and remained a significant predictor of long-term mortality at 4 years in a subset of patients (5, 6).Ina group of patients with chronic, severe heart failure from the Deventer-Alkmaar Heart Failure Study (DEAL-HF), baseline galectin-3 was a significant predictor of long-term mortality (at 6.5 years) after correcting for age, sex, renal dysfunction, and N-terminal pro-BNP (NT-proBNP) (7). The prognostic value of galectin-3 for predicting survival has been further validated in 4 subsequent studies of patients with chronic HF (8, 11), and it maybe particularly useful in patients with heart failure and preserved ejection fraction (8).

Our study broadens the potential role for galectin-3 as a prognostic biomarker in patients with established HF to those with acute ischemic heart disease at risk for future HF. Our observation of a relationship between galectin-3 and HF after ACS that was independent of clinical risk factors supports additional investigation of galectin-3 in this population. The absence of an incremental value over BNP may relate to the small sample size of our pilot study. Additional evaluation in larger studies would be needed to assess whether there is a useful clinical role for galectin-3 as a prognostic biomarker in patients with ischemic heart disease. However, the very weak pattern of correlation between galectin-3 and BNP argues against pure confounding by BNP and supports the experimental evidence that expression of these proteins is mediated by different biological pathways. Moreover, the finding that patients with elevation of both galectin-3 and BNP were at the highest risk for HF suggests potential incremental value from a strategy that uses both of these biomarkers for assessment of HF risk in patients post-ACS.


There are limitations to this study. The sample size was relatively small, which limited our ability to adjust for a more comprehensive list of heart failure risk factors. LVEF was collected only according to local practice, which limited our ability to fully assess the relationship between galectin-3, LVEF, and HF. We cannot rule out confounding by LVEF and that galectin-3 is simply a marker of deteriorating LV function. However, the correlation between LVEF and galectin-3, while significant, was weak, and galectin-3 was often increased in the absence of systolic dysfunction. In addition, clinical studies have shown that galectin-3 concentration does not correlate well with indices of LV size and function and is more strongly correlated with echocardiographic parameters of impaired diastolic relaxation and RV dysfunction (6, 9). Galectin-3 is a ubiquitously expressed protein that has been implicated in fibrosis of other organs, including the kidneys, liver, pancreas, and lungs, and our study does not establish whether serum galectin-3 concentration correlates with cardiac galectin-3 expression or myocardial collagen burden (20-25). We used a prespecified clinical definition of heart failure based on objective criteria, but in this large multinational clinical outcomes study, it was not feasible to require invasive evaluation of intracardiac pressures in all cases of suspected heart failure. Therefore, it is possible that in some patients other disease processes may have mimicked heart failure and contributed to elevation of galectin-3. However, based on our application of standardized criteria in this population with established ischemic heart disease, we expect any such contribution to have been small.


Our pilot study demonstrates that the serum concentration of galectin-3 is associated with the risk of developing HF after ACS and supports the potential clinical relevance of galectin-3-related pathways in patients with ischemic heart disease. Together with experimental data, our findings suggest that galectin-3 may merit consideration as a potential novel target for therapeutics to prevent HF in this population. Additional investigation is warranted to explore the mechanisms of this relationship in patients with ACS and to clarify whether there is sufficient incremental prognostic value to consider galectin-3 as part ofa screening strategy to identify patients with ischemic heart disease who are at higher risk of developing HF after ACS.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the Disclosures of Potential Conflict of Interest form. Potential conflicts of interest:

Employment or Leadership: D.A. Morrow, Clinical Chemistry, AACC.

Consultant or Advisory Role: P. Jarolim, Gerson Lehrman and T2 Biosystems; C.P. Cannon, BMS/Sanofi (funds donated to charity), Alnylam (funds donated to charity), and Novartis (funds donated to charity); D.A. Morrow, Beckman-Coulter, Instrumentation Laboratories, Siemens, Roche, OrthoClinical Diagnostics, Critical Diagnostics, Gilead, and Merck.

Stock Ownership: None declared.

Honoraria: P. Jarolim, Roche Diagnostics; C.P. Cannon, AstraZeneca (independent educational symposia) and Pfizer (independent educational symposia); D.A. Morrow, Roche. Research Funding: The PROVE IT-TIMI 22 trial was supported by Bristol-Myers Squibb/Sankyo. D.A. Morrow (principal investigator), BG Medicine.

Expert Testimony: None declared.

Role of Sponsor: The funding organizations played a direct role in the design of study.


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E. Wilson Grandin, [1] * Petr Jarolim, [3] Sabina A. Murphy, [1] Lea Ritterova, [3] Christopher P. Cannon, [1,2] Eugene Braunwald, [1,2] and David A. Morrow [1,2]

[1] TIMI Study Group, and [2] Cardiovascular Division, Department of Medicine, and [3] Department of Pathology, Brigham and Women's Hospital, Boston, MA.

[4] Nonstandard abbreviations: HF, heart failure; BNP, B-type natriuretic peptide; ACS, acute coronary syndrome; LVEF, left ventricular ejection fraction; PROVE IT-TIMI 22, Pravastatin or Atorvastatin Evaluation and Infection Therapy-- Thrombolysis in Myocardial Infarction 22; MI, myocardial infarction; p[O.sub.2], oxygen pressure; OR, odds ratio; TGF, transforming growth factor; PIIINP, type III amino-terminal propeptide of procollagen; MMP-2, matrix metalloproteinase 2; Ac-SDKP, N-acetyl-seryl-aspartyl-lysyl-proline; PRIDE, ProBNP Investigation of Dyspnea in the Emergency Department; DEAL-HF, Deventer-Alkmaar Heart Failure; NT-proBNP, N-terminal pro-BNP.

* Address correspondence to this author at: Department of Medicine, Brigham and Women's Hospital, 75 Francis St., Boston, MA 02115. Fax 617-734-7329; e-mail

Received August 23, 2011; accepted October 11, 2011.

Previously published online at DOI: 10.1373/clinchem.2011.174359
Table 1. Baseline characteristics of cases and controls.

Characteristic              Controls        Cases         P

n                             100            100
Mean age,                 65.7 (10.9)    65.7 (10.9)     1.0
  years (SD)
Male, %                        73             73         1.0
Hypertension, %                54             68        0.04
Diabetes, %                    23             32        0.15
Prior congestive               5              21        0.001
  heart failure, %
Prior MI, %                    25             39        0.03
ACE inhibitor/                 78             80        0.73
  blocker, %
Beta-blocker, %                90             82        0.10
LVEF <40%                      11 (a)         31 (b)    0.003
BNP >80 pg/mL, % (c)           20             41        0.002
Median peak creatine      6 (1-21) (d)   4 (1-14) (e)   0.43
  kinase MB, ng/mL
  (interquartile range)

(a) n = 74 with LVEF available.

(b) n = 65 with LVEF available.

(c) n = 188 with both biomarkers available.

(d) n = 72 with creatine kinase MB available.

(e) n = 70 with creatine kinase MB available.

Fig. 1. Univariate unadjusted
relative odds of heart failure
by galectin-3 quartile.

Galectin-3             Unadjusted
([micro]g/L)           OR

Referent   <13.0       1.0
P=0.10     13.0-15.5   2.0
P=0.03     15.6-19.2   2.6
P=0.004    >19.2       3.9

[P.sub.trend] = 0.003

Note: Table made from bar graph.

Fig. 3. Unadjusted odds of heart failure stratified by
galectin-3 (Gal3) at the median and BNP > 80 pg/mL.

Biomarker Profile     OR (95% CI)      P value

Low BNP, Low Gal3     1.0              Ref
Low BNP, High Gal3    1.5(0.7-3.0)     0 31
High BNP, Low Gal3    2.0 (0.7-5.4)    0.17
High BNP, High Gal3   4.7 (1.7-13.0)   0.002

Note: Table made from bar graph.
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Title Annotation:Proteomics and Protein Markets
Author:Grandin, E. Wilson; Jarolim, Petr; Murphy, Sabina A.; Ritterova, Lea; Cannon, Christopher P.; Braunw
Publication:Clinical Chemistry
Article Type:Report
Date:Jan 1, 2012
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